On the Relation Between Mobile Encounters and Web Traffic Patterns: A Data-driven Study

TL;DR

This study analyzes large-scale WiFi and NetFlow data to quantify the correlation between mobility encounters and web traffic patterns, revealing consistent relationships and potential for encounter prediction based on traffic profiles.

Contribution

It provides the first large-scale, data-driven analysis of the correlation between mobility encounters and web traffic, highlighting implications for future mobile services and privacy-preserving protocols.

Findings

Encountered pairs show higher traffic similarity than non-encountered pairs

Long encounters are associated with the highest traffic similarity

Mobility encounters can be partially predicted from web traffic profiles

Abstract

Mobility and network traffic have been traditionally studied separately. Their interaction is vital for generations of future mobile services and effective caching, but has not been studied in depth with real-world big data. In this paper, we characterize mobility encounters and study the correlation between encounters and web traffic profiles using large-scale datasets (30TB in size) of WiFi and NetFlow traces. The analysis quantifies these correlations for the first time, across spatio-temporal dimensions, for device types grouped into on-the-go Flutes and sit-to-use Cellos. The results consistently show a clear relation between mobility encounters and traffic across different buildings over multiple days, with encountered pairs showing higher traffic similarity than non-encountered pairs, and long encounters being associated with the highest similarity. We also investigate the feasibility of learning encounters through web traffic profiles, with implications for dissemination protocols, and contact tracing. This provides a compelling case to integrate both mobility and web traffic dimensions in future models, not only at an individual level, but also at pairwise and collective levels. We have released samples of code and data used in this study on GitHub, to support reproducibility and encourage further research (https://github.com/BabakAp/encounter-traffic).